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Traffic Data :, Historic Traffic Data, DNS Data, URL Data, Real-time…
Traffic Data :
Traffic Model
Beaconing Candidates
Ranking and Correlations
Risky Beaconing Candidates
Historic Traffic Data
Filter by Security Rule1
Sampling from Candidate1
Label by SME
Label argumentation
Train Traffic Classifier
Classifier Deployment for Online Pipeline
Filter by Security Rule 2
Sampling from Candidate2
Filter by Security Rule 3
Sampling from Candidate3
Filter by Security Rule 4
Sampling from Candidate4
Detect outliers
Clustering Algorithm
Sampling from Cluster 1
Sampling from Cluster 2
Sampling from Cluster 3
Labeling from SME
Labels defined by Security Rules
Building Generative Modeling
Labeling from Generative Models
DNS Data
DNS Model
Beaconing Candidates
URL Data
URL Model
Beaconing Candidates
Real-time Traffic Data
Filter by Security Rules
Apply Traffic Classifier
benign beaconing
collet customer feedback
suspicious beaconing
correlate URL and DNS models
collect customer feedback
highly suspicious beaconing
generate alerts
collect customer feedback
Incremental learning for model optimization
correlate URL and DNS models
Generate risk scores to customers